Abstract
Failure Modes and Effects Analysis (FMEA) is a qualitative and quantitative approach to measuring and analyzing risk that compiles and ranks failure modes, their effects, and their corrective actions. Though widely used, traditional FMEA has been criticized for the lack of a scientific basis behind the Risk Priority Number calculation. To combat this, researchers have argued that Multiple Criteria Decision Making (MCDM) methods should be used to rank failure modes instead. As such, the current paper was developed to present a case study that applies FMEA and MCDM to a Central Venous Catheterization (CVC) training simulator called the Dynamic Haptic Robotic Trainer (DHRT). FMEA was needed because while a beta prototype exists for research purposes, there are several failure modes that prevent this system from wide spread deployment. Our results provide insight into how FMEA can be used to identify a system’s highest priority failure modes and maximize improvement recommendations.